




'''simple docstring'''







import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )
    from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
    import torch

class 	_snake_case:






 def __init__(self       :  Optional[int]    ,		a       :  int    ,		a       :  Any=14    ,		a       :  Any=7    ,		a       :  Dict=True    ,		a       :  Dict=True    ,		a       :  Dict=False    ,		a       :  Union[str, Any]=True    ,		a       :  Optional[int]=99    ,		a       :  Union[str, Any]=32    ,		a       :  int=4    ,		a       :  Any=4    ,		a       :  str=4    ,		a       :  Any=37    ,		a       :  List[str]="gelu"    ,		a       :  Union[str, Any]=0.1    ,		a       :  List[Any]=0.1    ,		a       :  Union[str, Any]=5_12    ,		a       :  Dict=0.02    ,		)       ->   Optional[int]:






    """simple docstring"""


    A__       =     parent
    A__       =     batch_size
    A__       =     seq_length
    A__       =     is_training
    A__       =     use_input_mask
    A__       =     use_token_type_ids
    A__       =     use_labels
    A__       =     vocab_size
    A__       =     hidden_size
    A__       =     rotary_dim
    A__       =     num_hidden_layers
    A__       =     num_attention_heads
    A__       =     intermediate_size
    A__       =     hidden_act
    A__       =     hidden_dropout_prob
    A__       =     attention_probs_dropout_prob
    A__       =     max_position_embeddings
    A__       =     initializer_range
    A__       =     None
    A__       =     vocab_size - 1
    A__       =     vocab_size - 1
    A__       =     vocab_size - 1






 def 						_UpperCamelCase (self       :  Dict    )       ->   Any:






    """simple docstring"""


    A__       =     ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size    )

    A__       =     None
    if self.use_input_mask:
       A__       =     random_attention_mask([self.batch_size, self.seq_length]    )

    A__       =     GPTJConfig(
        vocab_size=self.vocab_size    ,		n_embd=self.hidden_size    ,		n_layer=self.num_hidden_layers    ,		n_head=self.num_attention_heads    ,		n_positions=self.max_position_embeddings    ,		use_cache=a    ,		bos_token_id=self.bos_token_id    ,		eos_token_id=self.eos_token_id    ,		pad_token_id=self.pad_token_id    ,		rotary_dim=self.rotary_dim    ,		)

    return (config, input_ids, input_mask)






 def 						_UpperCamelCase (self       :  str    )       ->   int:






    """simple docstring"""


    A__       =     self.prepare_config_and_inputs()
    A__  ,			A__  ,			A__       =     config_and_inputs
    A__       =     {'input_ids': input_ids, 'attention_mask': attention_mask}
    return config, inputs_dict






 def 						_UpperCamelCase (self       :  Any    ,		a       :  Optional[int]    ,		a       :  Dict    ,		a       :  List[str]    ,		a       :  str    )       ->   List[Any]:






    """simple docstring"""


    A__       =     20
    A__       =     model_class_name(a    )

    A__       =     model.init_cache(input_ids.shape[0]    ,		a    )
    A__       =     jnp.ones((input_ids.shape[0], max_decoder_length)    ,		dtype='i4'    )

    A__       =     jnp.broadcast_to(
        jnp.arange(input_ids.shape[-1] - 1    )[None, :]    ,		(input_ids.shape[0], input_ids.shape[-1] - 1)    )
    A__       =     model(
        input_ids[:, :-1]    ,		attention_mask=a    ,		past_key_values=a    ,		position_ids=a    ,		)

    A__       =     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,		dtype='i4'    )
    A__       =     model(
        input_ids[:, -1:]    ,		attention_mask=a    ,		past_key_values=outputs_cache.past_key_values    ,		position_ids=a    ,		)

    A__       =     model(a    )

    A__       =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
    self.parent.assertTrue(diff < 1e-3    ,		msg=f"""Max diff is {diff}"""    )






 def 						_UpperCamelCase (self       :  Dict    ,		a       :  List[Any]    ,		a       :  Union[str, Any]    ,		a       :  Dict    ,		a       :  Optional[int]    )       ->   int:






    """simple docstring"""


    A__       =     20
    A__       =     model_class_name(a    )

    A__       =     jnp.concatenate(
        [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])    )]    ,		axis=-1    ,		)

    A__       =     model.init_cache(input_ids.shape[0]    ,		a    )
    A__       =     jnp.broadcast_to(
        jnp.arange(input_ids.shape[-1] - 1    )[None, :]    ,		(input_ids.shape[0], input_ids.shape[-1] - 1)    )

    A__       =     model(
        input_ids[:, :-1]    ,		attention_mask=a    ,		past_key_values=a    ,		position_ids=a    ,		)
    A__       =     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,		dtype='i4'    )
    A__       =     model(
        input_ids[:, -1:]    ,		past_key_values=outputs_cache.past_key_values    ,		attention_mask=a    ,		position_ids=a    ,		)

    A__       =     model(a    ,		attention_mask=a    )

    A__       =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
    self.parent.assertTrue(diff < 1e-3    ,		msg=f"""Max diff is {diff}"""    )




@require_flax
class 	_snake_case(			UpperCAmelCase       , UpperCAmelCase       , unittest.TestCase      ):
 __snake_case:							Optional[int]						=					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
 __snake_case:							int						=					(FlaxGPTJForCausalLM,) if is_flax_available() else ()






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   List[str]:






    """simple docstring"""


    A__       =     FlaxGPTJModelTester(self    )






 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Optional[Any]:






    """simple docstring"""


    for model_class_name in self.all_model_classes:
       A__  ,			A__  ,			A__       =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(a    ,		a    ,		a    ,		a    )






 def 						_UpperCamelCase (self       :  Any    )       ->   Union[str, Any]:






    """simple docstring"""


    for model_class_name in self.all_model_classes:
       A__  ,			A__  ,			A__       =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward_with_attn_mask(
           a    ,		a    ,		a    ,		a    )






 @tooslow
 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     GPTaTokenizer.from_pretrained('gpt2'    ,		pad_token='<|endoftext|>'    ,		padding_side='left'    )
    A__       =     tokenizer(['Hello this is a long string', 'Hey']    ,		return_tensors='np'    ,		padding=a    ,		truncation=a    )

    A__       =     FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'    )
    A__       =     False
    A__       =     model.config.eos_token_id

    A__       =     jax.jit(model.generate    )

    A__       =     jit_generate(
        inputs['input_ids']    ,		attention_mask=inputs['attention_mask']    ,		pad_token_id=tokenizer.pad_token_id    ).sequences

    A__       =     tokenizer.batch_decode(a    ,		skip_special_tokens=a    )

    A__       =     [
        'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
        'Hey, I\'m a little late to the party. I\'m going to',
    ]

    self.assertListEqual(a    ,		a    )






 @is_pt_flax_cross_test
 def 						_UpperCamelCase (self       :  str    )       ->   Dict:






    """simple docstring"""


    A__  ,			A__       =     self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__    ):
          # prepare inputs
          A__       =     self._prepare_for_class(a    ,		a    )
          A__       =     {k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

          # load corresponding PyTorch class
          A__       =     model_class.__name__[4:]  # Skip the "Flax" at the beginning
          A__       =     getattr(a    ,		a    )

          A__  ,			A__       =     pt_inputs['input_ids'].shape
          A__       =     np.random.randint(0    ,		seq_length - 1    ,		size=(batch_size,)    )
          for batch_idx, start_index in enumerate(a    ):
             A__       =     0
             A__       =     1
             A__       =     0
             A__       =     1
          A__       =     pt_model_class(a    ).eval()
          A__       =     model_class(a    ,		dtype=jnp.floataa    )

          A__       =     convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,		a    )
          A__       =     fx_state

          with torch.no_grad():
             A__       =     pt_model(**a    ).to_tuple()

          A__       =     fx_model(**a    ).to_tuple()
          self.assertEqual(len(a    )    ,		len(a    )    ,		'Output lengths differ between Flax and PyTorch'    )
          for fx_output, pt_output in zip(a    ,		a    ):
             self.assert_almost_equals(fx_output[:, -1]    ,		pt_output[:, -1].numpy()    ,		4e-2    )

          with tempfile.TemporaryDirectory() as tmpdirname:
             pt_model.save_pretrained(a    )
             A__       =     model_class.from_pretrained(a    ,		from_pt=a    )

          A__       =     fx_model_loaded(**a    ).to_tuple()
          self.assertEqual(
              len(a    )    ,		len(a    )    ,		'Output lengths differ between Flax and PyTorch'    )
          for fx_output_loaded, pt_output in zip(a    ,		a    ):
             self.assert_almost_equals(fx_output_loaded[:, -1]    ,		pt_output[:, -1].numpy()    ,		4e-2    )






 @is_pt_flax_cross_test
 def 						_UpperCamelCase (self       :  str    )       ->   Optional[int]:






    """simple docstring"""


    A__  ,			A__       =     self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__    ):
          # prepare inputs
          A__       =     self._prepare_for_class(a    ,		a    )
          A__       =     {k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

          # load corresponding PyTorch class
          A__       =     model_class.__name__[4:]  # Skip the "Flax" at the beginning
          A__       =     getattr(a    ,		a    )

          A__       =     pt_model_class(a    ).eval()
          A__       =     model_class(a    ,		dtype=jnp.floataa    )

          A__       =     load_flax_weights_in_pytorch_model(a    ,		fx_model.params    )
          A__  ,			A__       =     pt_inputs['input_ids'].shape
          A__       =     np.random.randint(0    ,		seq_length - 1    ,		size=(batch_size,)    )
          for batch_idx, start_index in enumerate(a    ):
             A__       =     0
             A__       =     1
             A__       =     0
             A__       =     1

          # make sure weights are tied in PyTorch
          pt_model.tie_weights()

          with torch.no_grad():
             A__       =     pt_model(**a    ).to_tuple()

          A__       =     fx_model(**a    ).to_tuple()
          self.assertEqual(len(a    )    ,		len(a    )    ,		'Output lengths differ between Flax and PyTorch'    )
          for fx_output, pt_output in zip(a    ,		a    ):
             self.assert_almost_equals(fx_output[:, -1]    ,		pt_output[:, -1].numpy()    ,		4e-2    )

          with tempfile.TemporaryDirectory() as tmpdirname:
             fx_model.save_pretrained(a    )
             A__       =     pt_model_class.from_pretrained(a    ,		from_flax=a    )

          with torch.no_grad():
             A__       =     pt_model_loaded(**a    ).to_tuple()

          self.assertEqual(
              len(a    )    ,		len(a    )    ,		'Output lengths differ between Flax and PyTorch'    )
          for fx_output, pt_output in zip(a    ,		a    ):
             self.assert_almost_equals(fx_output[:, -1]    ,		pt_output[:, -1].numpy()    ,		4e-2    )






 @tooslow
 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Optional[int]:






    """simple docstring"""


    for model_class_name in self.all_model_classes:
       A__       =     model_class_name.from_pretrained('EleutherAI/gpt-j-6B'    )
       A__       =     model(np.ones((1, 1)    )    )
       self.assertIsNotNone(a    )








'''simple docstring'''







import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=True      ,UpperCAmelCase="pt"						):






   '''simple docstring'''







   A__       =     {'add_prefix_space': True} if isinstance(UpperCAmelCase      ,UpperCAmelCase						) and not line.startswith(' '						) else {}
   A__       =     padding_side
   return tokenizer(
       [line]      ,max_length=UpperCAmelCase      ,padding='max_length' if pad_to_max_length else None      ,truncation=UpperCAmelCase      ,return_tensors=UpperCAmelCase      ,add_special_tokens=UpperCAmelCase      ,**UpperCAmelCase      ,)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=None      ,):






   '''simple docstring'''







   A__       =     input_ids.ne(UpperCAmelCase						).any(dim=0						)
   if attention_mask is None:
      return input_ids[:, keep_column_mask]
   else:
      return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])

class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  List[str]    ,		a       :  Any    ,		a       :  Union[str, Any]    ,		a       :  int    ,		a       :  Optional[int]    ,		a       :  Optional[Any]="train"    ,		a       :  Optional[int]=None    ,		a       :  Tuple=None    ,		a       :  str=None    ,		a       :  Dict=""    ,		)       ->   Dict:






    """simple docstring"""


    super().__init__()
    A__       =     Path(a    ).joinpath(type_path + '.source'    )
    A__       =     Path(a    ).joinpath(type_path + '.target'    )
    A__       =     self.get_char_lens(self.src_file    )
    A__       =     max_source_length
    A__       =     max_target_length
    assert min(self.src_lens    ) > 0, f"""found empty line in {self.src_file}"""
    A__       =     tokenizer
    A__       =     prefix
    if n_obs is not None:
       A__       =     self.src_lens[:n_obs]
    A__       =     src_lang
    A__       =     tgt_lang






 def __len__(self       :  Optional[Any]    )       ->   List[str]:






    """simple docstring"""


    return len(self.src_lens    )






 def __getitem__(self       :  Any    ,		a       :  str    )       ->   Dict[str, torch.Tensor]:






    """simple docstring"""


    A__       =     index + 1  # linecache starts at 1
    A__       =     self.prefix + linecache.getline(str(self.src_file    )    ,		a    ).rstrip('\n'    )
    A__       =     linecache.getline(str(self.tgt_file    )    ,		a    ).rstrip('\n'    )
    assert source_line, f"""empty source line for index {index}"""
    assert tgt_line, f"""empty tgt line for index {index}"""

    # Need to add eos token manually for T5
    if isinstance(self.tokenizer    ,		a    ):
       source_line += self.tokenizer.eos_token
       tgt_line += self.tokenizer.eos_token

    # Pad source and target to the right
    A__       =     (
        self.tokenizer.question_encoder if isinstance(self.tokenizer    ,		a    ) else self.tokenizer
    )
    A__       =     self.tokenizer.generator if isinstance(self.tokenizer    ,		a    ) else self.tokenizer

    A__       =     encode_line(a    ,		a    ,		self.max_source_length    ,		'right'    )
    A__       =     encode_line(a    ,		a    ,		self.max_target_length    ,		'right'    )

    A__       =     source_inputs['input_ids'].squeeze()
    A__       =     target_inputs['input_ids'].squeeze()
    A__       =     source_inputs['attention_mask'].squeeze()
    return {
        "input_ids": source_ids,
        "attention_mask": src_mask,
        "decoder_input_ids": target_ids,
    }






 @staticmethod
 def 						_UpperCamelCase (a       :  Tuple    )       ->   Tuple:






    """simple docstring"""


    return [len(a    ) for x in Path(a    ).open().readlines()]






 def 						_UpperCamelCase (self       :  str    ,		a       :  int    )       ->   Dict[str, torch.Tensor]:






    """simple docstring"""


    A__       =     torch.stack([x['input_ids'] for x in batch]    )
    A__       =     torch.stack([x['attention_mask'] for x in batch]    )
    A__       =     torch.stack([x['decoder_input_ids'] for x in batch]    )
    A__       =     (
        self.tokenizer.generator.pad_token_id
        if isinstance(self.tokenizer    ,		a    )
        else self.tokenizer.pad_token_id
    )
    A__       =     (
        self.tokenizer.question_encoder.pad_token_id
        if isinstance(self.tokenizer    ,		a    )
        else self.tokenizer.pad_token_id
    )
    A__       =     trim_batch(a    ,		a    )
    A__  ,			A__       =     trim_batch(a    ,		a    ,		attention_mask=a    )
    A__       =     {
        'input_ids': source_ids,
        'attention_mask': source_mask,
        'decoder_input_ids': y,
    }
    return batch




lowerCAmelCase_     						=				getLogger(__name__)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   return list(itertools.chain.from_iterable(UpperCAmelCase						)						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     get_git_info()
   save_json(UpperCAmelCase      ,os.path.join(UpperCAmelCase      ,'git_log.json'						)						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=4      ,**UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase      ,'w'						) as f:
      json.dump(UpperCAmelCase      ,UpperCAmelCase      ,indent=UpperCAmelCase      ,**UpperCAmelCase						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase						) as f:
      return json.load(UpperCAmelCase						)







def 						_A      ( ):






   '''simple docstring'''







   A__       =     git.Repo(search_parent_directories=UpperCAmelCase						)
   A__       =     {
       'repo_id': str(UpperCAmelCase						),
       'repo_sha': str(repo.head.object.hexsha						),
       'repo_branch': str(repo.active_branch						),
       'hostname': str(socket.gethostname()						),
   }
   return repo_infos







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   return list(map(UpperCAmelCase      ,UpperCAmelCase						)						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase      ,'wb'						) as f:
      return pickle.dump(UpperCAmelCase      ,UpperCAmelCase						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   def remove_articles(UpperCAmelCase						):
      return re.sub(r'\b(a|an|the)\b'      ,' '      ,UpperCAmelCase						)

   def white_space_fix(UpperCAmelCase						):
      return " ".join(text.split()						)

   def remove_punc(UpperCAmelCase						):
      A__       =     set(string.punctuation						)
      return "".join(ch for ch in text if ch not in exclude						)

   def lower(UpperCAmelCase						):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase						)						)						)						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     normalize_answer(UpperCAmelCase						).split()
   A__       =     normalize_answer(UpperCAmelCase						).split()
   A__       =     Counter(UpperCAmelCase						) & Counter(UpperCAmelCase						)
   A__       =     sum(common.values()						)
   if num_same == 0:
      return 0
   A__       =     1.0 * num_same / len(UpperCAmelCase						)
   A__       =     1.0 * num_same / len(UpperCAmelCase						)
   A__       =     (2 * precision * recall) / (precision + recall)
   return fa







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   return normalize_answer(UpperCAmelCase						) == normalize_answer(UpperCAmelCase						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   assert len(UpperCAmelCase						) == len(UpperCAmelCase						)
   A__       =     0
   for hypo, pred in zip(UpperCAmelCase      ,UpperCAmelCase						):
      em += exact_match_score(UpperCAmelCase      ,UpperCAmelCase						)
   if len(UpperCAmelCase						) > 0:
      em /= len(UpperCAmelCase						)
   return {"em": em}







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   return model_prefix.startswith('rag'						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     {p: p for p in extra_params}
   # T5 models don't have `dropout` param, they have `dropout_rate` instead
   A__       =     'dropout_rate'
   for p in extra_params:
      if getattr(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):
         if not hasattr(UpperCAmelCase      ,UpperCAmelCase						) and not hasattr(UpperCAmelCase      ,equivalent_param[p]						):
            logger.info('config doesn\'t have a `{}` attribute'.format(UpperCAmelCase						)						)
            delattr(UpperCAmelCase      ,UpperCAmelCase						)
            continue
         A__       =     p if hasattr(UpperCAmelCase      ,UpperCAmelCase						) else equivalent_param[p]
         setattr(UpperCAmelCase      ,UpperCAmelCase      ,getattr(UpperCAmelCase      ,UpperCAmelCase						)						)
         delattr(UpperCAmelCase      ,UpperCAmelCase						)
   return hparams, config


